Ferroptosis-related genes in cervical cancer as biomarkers for predicting the prognosis of gynecological tumors - Han, Songtao; Wang, Senyu; Lv, Xiang; Li, Dan; Feng, Yangchun (Frontiers in Molecular Biosciences, 2023)
Background Ferroptosis has been identified as a potent predictor of cancer prognosis. Currently, cervical cancer ranks among the most prevalent malignant tumors in women. Enhancing the prognosis for patients experiencing metastasis or recurrence is of critical importance. Consequently, investigating the potential of ferroptosis-related genes (FRGs) as prognostic biomarkers for cervical cancer patients is essential. Methods In this study, 52 FRGs were obtained from the GSE9750, GSE7410, GSE63514, and FerrDb databases. Six genes possessing prognostic characteristics were identified: JUN, TSC22D3, SLC11A2, DDIT4, DUOX1, and HELLS. The multivariate Cox regression analysis was employed to establish and validate the prognostic model, while simultaneously performing a correlation analysis of the immune microenvironment. Results The prediction model was validated using TCGA-CESC and GSE44001 datasets. Furthermore, the prognostic model was validated in endometrial cancer and ovarian serous cystadenocarcinoma cases. KM curves revealed significant differences in OS between high-risk and low-risk groups. ROC curves demonstrated the stability and accuracy of the prognostic model established in this study. Concurrently, the research identified a higher proportion of immune cells in patients within the low-risk group. Additionally, the expression of immune checkpoints (TIGIT, CTLA4, BTLA, CD27, and CD28) was elevated in the low-risk group. Ultimately, 4 FRGs in cervical cancer were corroborated through qRT-PCR. Conclusion The FRGs prognostic model for cervical cancer not only exhibits robust stability and accuracy in predicting the prognosis of cervical cancer patients but also demonstrates considerable prognostic value in other gynecological tumors.